User equipment using random access channel (rach) parameters to adjust transmit power, and method of using the rach parameters to adjust transmit power

ABSTRACT

The user equipment includes a processor configured to receive a broadcast message, the broadcast message including RACH parameters. The processor transmits a first message following receipt of the broadcast message. The processor receives a second message. The processor transmits a third message, the third message including power settings based on a successful transmission of the first message, and receives a fourth message, the fourth message including contention resolution information that adjusts the RACH parameters. The user equipment decodes RACH parameters and the contention information to obtain decoded RACH parameters, and adjusts an initial transmit power of the user equipment to be a first transmit power based on the decoded RACH parameters.

BACKGROUND OF THE INVENTION Field of the Invention

Example embodiments relate generally to a system and method forcontrolling network traffic using improved preamble detection and anautomation of determined random access channel parameters.

Related Art

FIG. 1 illustrates a conventional 3rd Generation Partnership ProjectLong-Term Evolution (3GPP LTE) network 10. The network 10 includes anInternet Protocol (IP) Connectivity Access Network (IP-CAN) 100 and anIP Packet Data Network (IP-PDN) 1001. The IP-CAN 100 generally includes:a serving gateway (SGW) 101; a packet data network (PDN) gateway (PGW)103; a policy and charging rules function (PCRF) 106; a mobilitymanagement entity (MME) 108 and E-UTRAN Node B (eNB) 105 (i.e., basestation, for the purposes herein the terms base station and eNB may beused interchangeably). Although not shown, the IP-PDN 1001 portion ofthe EPS may include application or proxy servers, media servers, emailservers, etc.

Within the IP-CAN 100, the eNB 105 is part of what is referred to as anEvolved Universal Mobile Telecommunications System (UMTS) TerrestrialRadio Access Network (EUTRAN), and the portion of the IP-CAN 100including the SGW 101, the PGW 103, the PCRF 106, and the MME 108 isreferred to as an Evolved Packet Core (EPC). Although only a single eNB105 is shown in FIG. 1, it should be understood that the EUTRAN mayinclude any number of eNBs. Similarly, although only a single SGW, PGWand MME are shown in FIG. 1, it should be understood that the EPC mayinclude any number of these core network elements.

The eNB 105 provides wireless resources and radio coverage for one ormore user equipments (UEs) 110. That is to say, any number of UEs 110may be connected (or attached) to the eNB 105. The eNB 105 isoperatively coupled to the SGW 101 and the MME 108.

The SGW 101 routes and forwards user data packets, while also acting asthe mobility anchor for the user plane during inter-eNB handovers ofUEs. The SGW 101 also acts as the anchor for mobility between 3rdGeneration Partnership Project Long-Term Evolution (3GPP LTE) and other3GPP technologies. For idle UEs 110, the SGW 101 terminates the downlinkdata path and triggers paging when downlink data arrives for UEs 110.

The PGW 103 provides connectivity between UE 110 and the external packetdata networks (e.g., the IP-PDN) by being the point of entry/exit oftraffic for the UE 110. As is known, a given UE 110 may havesimultaneous connectivity with more than one PGW 103 for accessingmultiple PDNs.

The PGW 103 also performs policy enforcement, packet filtering for UEs110, charging support, lawful interception and packet screening, each ofwhich are well-known functions. The PGW 103 also acts as the anchor formobility between 3GPP and non-3GPP technologies, such as WorldwideInteroperability for. Microwave Access (WiMAX) and 3rd GenerationPartnership Project 2 (3GPP2 (code division multiple access (CDMA) 1×and Enhanced Voice Data Optimized (EvDO)).

Still referring to FIG. 1, eNB 105 is also operatively coupled to theMME 108. The MME 108 is the control-node for the EUTRAN, and isresponsible for idle mode UE 110 paging and tagging procedures includingretransmissions. The MME 108 is also responsible for choosing aparticular SGW for a UE during initial attachment of the UE to thenetwork, and during intra-LTE handover involving Core Network (CN) noderelocation. The MME 108 authenticates UEs 110 by interacting with a HomeSubscriber Server (HSS), which is not shown in FIG. 1.

Non Access Stratum (NAS) signaling terminates at the MME 108, and isresponsible for generation and allocation of temporary identities forUEs 110. The MME 108 also checks the authorization of a UE 110 to campon a service provider's Public Land Mobile Network (PLMN), and enforcesUE 110 roaming restrictions. The MME 108 is the termination point in thenetwork for ciphering/integrity protection for NAS signaling, andhandles security key management.

The MME 108 also provides control plane functionality for mobilitybetween LTE and 2G/3G access networks with an interface from the SGSN(not shown) terminating at the MME 108.

The Policy and Charging Rules Function (PCRF) 106 is the entity that mayaccess subscriber databases, make policy decisions and set chargingrules for the subscriber.

FIG. 2 illustrates a conventional E-UTRAN Node B (eNB) 105. The eNB 105includes: a memory 225; a processor 210; a scheduler 215; wirelesscommunication interfaces 220; radio link control (RLC) buffers 230 foreach bearer; and a backhaul interface 235. The processor 210 may also bereferred to as a core network entity processing circuit, an EPC entityprocessing circuit, etc. The processor 210 may consist of one or morecore processing units, either physically coupled together ordistributed. The processor 210 controls the function of eNB 105 (asdescribed herein), and is operatively coupled to the memory 225 and thecommunication interfaces 220. While only one processor 210 is shown inFIG. 2, it should be understood that multiple processors may be includedin a typical eNB 105. The functions performed by the processor may beimplemented using hardware. Such hardware may include one or moreCentral Processing Units (CPUs), digital signal processors (DSPs),application-specific-integrated-circuits, field programmable gate arrays(FPGAs) computers or the like. The term processor, used throughout thisdocument, may refer to any of these example implementations, though theterm is not limited to these examples. With a Virtual Radio AccessNetwork (VRAN) architecture various functions eNB components may bedistributed across multiple processing circuits and multiple physicalnodes within VRAN cloud.

The eNB 105 may include one or more cells or sectors serving UEs 110within individual geometric coverage sector areas. Each cellindividually may contain elements depicted in FIG. 2. Throughout thisdocument the terms eNB, cell or sector shall be used interchangeably.

Still referring to FIG. 2, the wireless communication interfaces 220include various interfaces including one or more transmitters/receiversconnected to one or more antennas to transmit/receive wirelessly controland data signals to/from UEs 110. Backhaul interface 235 is the portionof eNB 105 that interfaces with SGW 101, MME 108, other eNBs, orinterface to other EPC network elements and/or RAN elements withinIP-CAN 100. The scheduler 215 schedules control and data communicationsthat are to be transmitted and received by the eNB 105 to and from UEs110. The memory 225 may buffer and store data that is being processed ateNB 105, transmitted and received to and from eNB 105.

Scheduler 215 may make physical resource block (PRB) allocationdecisions based upon a Quality of Service (QoS) Class Identifier (QCI),which represents traffic priority hierarchy. There are nine QCI classescurrently defined in LTE, with 1 representing highest priority and 9representing the lowest priority. QCIs 1 to 4 are reserved forGuaranteed Bitrate (GBR) classes for which the scheduler maintainscertain specific data flow QoS characteristics. QCIs 5 to 9 are reservedfor various categories of Best Effort traffic.

A random access channel (RACH) enables user equipments (UEs) 110 toperform tasks such as initially accessing the communication network 10,uplink synchronization, handovers between cells, and recovery fromfailed links. Therefore, an achievement of an optimal random accessperformance through an efficient RACH signature detection algorithm, anduse of a correct configuration of the RACH parameters, is crucial tooptimizing performance of the communication network.

Conventional random access channel (RACH) preamble detectors, whichtakes place in the wireless interface 220 of an eNB 105, relies on anestimation of a noise floor in order to set a preamble detectionthreshold. Because of the non-stationary nature of signals involved inRACH preamble detection due to random RACH transmissions, unpredictablescheduling decisions and non-Gaussian interference, sufficientlyaccurate estimation of the noise floor may be an unachievable task andthis may lead to an underperforming cellular system.

Conventional solutions for configuring RACH parameters generally consistof static parameter settings based on engineering best practices. Thatis to say, these static parameter settings may be performed vialink-budget, traffic engineering calculations and/or field performancemeasurements. However, a static parameter configuration solution mayexperience several shortcomings. First, static parameter configurationsmay not have the ability to overcome possible mismatches between thecalculations used to set the parameters and the realities of areal-world deployment. Second, static configurations may not adapt tochanging conditions in a cellular network, especially with regard tointerference and traffic intensity. Third, static configurations may notautonomously tune the RACH parameters to reflect changes in the networkarchitecture over time, such as cell splitting and the insertion ofsmall cells.

Conventional solutions do not efficiently avoid interference ofcommunication channels, optimize RACH coverage, minimize delays relatedto call setup and handover, reduce network signaling overhead, andobtain an optimal resource allocation balance between random access andother network communication needs. Additionally, high operationalexpenditures are often experienced in order to maintain communicationnetworks when the RACH parameters are statically configured becausefrequent human intervention is generally required.

SUMMARY OF INVENTION

At least one example embodiment relates to a method of preambledetection to control network traffic in a communication network.

In one example embodiment, the method of preamble detection to controlnetwork traffic in a communication network includes detecting, by atleast a first processor of at least a first network node, physicalrandom access channel (RACH) preambles, by performing the steps of,computing a correlation power profile based on a set of received RACHpreambles, sorting correlation power profile values, computing a weightfactor for each of the correlation power profile values based on anormalized RACH detection threshold, selecting outlier peaks of thecorrelation power profile values based on the weight factor, mapping theoutlier peaks to the first set of RACH signatures in order to identifyat least one UE that is associated with one of the received RACHpreambles; and controlling, by the first processor, the network trafficof network communications associated with the at least one identifiedUE.

In one example embodiment, the method includes detecting of the RACHpreambles by using a forward consecutive mean excision (FCME) method.

In one example embodiment, the detecting of the RACH preambles furtherincludes, collecting noise rise information and preamble missinformation for a set of transmitted RACH preambles, computing anoverall average noise rise for the communication network based on thecollected noise rise information, computing an average missed detectionratio for the communication network using the collected preamble missinformation, determining a target receiver power and a power ramp-upusing the average nose rise and the average missed detection ratio.

In one example embodiment, the detecting of the RACH preambles furtherincludes determining the normalized RACH detection threshold byperforming the steps of, collecting preamble false alarm ratioinformation, computing an average false alarm rate for the communicationnetwork based on the preamble false alarm ratio information, anddetermining the normalized RACH detection threshold based on the averagefalse alarm rate, wherein the controlling the network traffic furtherincludes, broadcasting the normalized RACH detection threshold to UEs ofthe communication network.

In one example embodiment, the method further includes collecting acontention ratio information, computing an average contention ratio forthe communication network based on the collected contention ratioinformation, determining the opportunity period based on the averagecontention ratio, wherein the controlling the network traffic furtherincludes, broadcasting the opportunity period to UEs of thecommunication network.

In one example embodiment, the method includes repeating the steps ofcollecting noise rise information and preamble miss information, andrepeating the steps of computing an overall average noise rise and anaverage missed detection ratio until the determined target receiverpower and the power ramp-up remain constant; wherein the controlling thenetwork traffic further includes, broadcasting the target receiver powerand a power ramp-up to UEs of the communication network, and wherein thedetecting of the RACH preambles further includes determining thenormalized RACH detection threshold, once the determined target receiverpower and the power ramp-up remain constant, by performing the steps of,collecting preamble false alarm ratio information, computing an averagefalse alarm rate for the communication network based on the preamblefalse alarm ratio information, and determining the normalized RACHdetection threshold based on the average false alarm rate, wherein thecontrolling the network traffic further includes, broadcasting thenormalized RACH detection threshold to UEs of the communication network.

In one example embodiment, the method further includes repeating thesteps of collecting preamble false alarm ratio information and computingan average false alarm rate until the determined normalized RACHdetection threshold remains constant; and wherein the detecting of theRACH preambles further includes determining an opportunity period, oncethe determined target receiver power, the power ramp-up, and thenormalized RACH detection threshold remain constant, by performing thesteps of, collecting a contention ratio information, computing anaverage contention ratio for the communication network based on thecollected contention ratio information, determining the opportunityperiod based on the average contention ratio, wherein the controllingthe network traffic further includes, broadcasting the opportunityperiod to UEs of the communication network.

In one example embodiment, the method includes the at least a firstprocessor of at least a first network node being a plurality ofprocessors, each of the plurality of the processors being associatedwith a network node dedicated to a respective sector within thecommunication network, the detecting of the RACH preambles furtherincludes, each of the processors collecting noise rise information andpreamble miss information for a set of transmitted RACH preambles forthe respective sector, each of the processors computing an overallaverage noise rise based on the collected noise rise information for therespective sector, each of the processors computing an average misseddetection ratio, for the respective sectors, using the collectedpreamble miss information from each respective sector, each of theprocessors sharing the overall average noise rise and the average misseddetection ratio with at least one of the other processors, of theplurality of processors, which is a neighbor processor, each of theprocessors determining a target receiver power and a power ramp-up, forthe respective sectors, using the shared average nose rise and theshared average missed detection ratio.

In one example embodiment, the method further includes repeating thesteps of each of the processors of the respective sectors collectingnoise rise information and preamble miss information, and repeating thestep of computing an overall average noise rise and an average misseddetection ratio until the determined target receiver power and the powerramp-up remain constant; wherein the controlling the network trafficfurther includes, each of the processors broadcasting the targetreceiver power and a power ramp-up to UEs for the respective sectors,and wherein the detecting of the RACH preambles further includesdetermining the normalized RACH detection threshold, once the determinedtarget receiver power and the power ramp-up remain constant, byperforming the steps of, each of the processors collecting preamblefalse alarm ratio information for the respective sectors, each of theprocessor computing an average false alarm rate, for the respectivesectors, based on the preamble false alarm ratio information, and eachof the processors determining the normalized RACH detection threshold,for the respective sectors, based on the average false alarm rate,wherein the controlling of the network traffic further includes each ofthe processors broadcasting the normalized RACH detection threshold toUEs for the respective sectors.

In one example embodiment, the method further includes repeating thesteps of each of the processors of the respective sectors collectingpreamble false alarm ratio information and computing an average falsealarm rate, until the determined normalized RACH detection thresholdremains constant; wherein the detecting of the RACH preambles furtherincludes determining the opportunity period, once the determinednormalized RACH detection threshold remains constant, by performing thesteps of, each of the processors collecting a contention ratioinformation for the respective sectors, computing an average contentionratio for the respective sectors based on the collected contention ratioinformation, each of the processors determining the opportunity period,for the respective sectors, based on the average contention ratio; andwherein the controlling of the network traffic further includes each ofthe processors broadcasting the target receiver power, the powerramp-up, and the opportunity period to the UEs for the respectivesectors.

At least another example embodiment relates to at least a first networknode in a communication network.

In one example embodiment, the at least a first network node includes atleast a first processor, configured to, detect physical random accesschannel (RACH) preambles, by performing the steps of, computing acorrelation power profile based on a set of received RACH preambles,sorting correlation power profile values, computing a weight factor foreach of the correlation power profile values based on a normalized RACHdetection threshold, selecting outlier peaks of the correlation powerprofile values based on the weight factor, mapping the outlier peaks tothe first set of RACH signatures in order to identify at least one UEthat is associated with one of the received RACH preambles; and controlnetwork traffic of network communications associated with the at leastone identified UE.

In one example embodiment, the at least a first processor is furtherconfigured to detect the RACH preambles by using a forward consecutivemean excision (FCME) method.

In one example embodiment, the at least a first processor is furtherconfigured to detect the RACH preambles by, collecting noise riseinformation and preamble miss information for a set of transmitted RACHpreambles, computing an overall average noise rise for the communicationnetwork based on the collected noise rise information, computing anaverage missed detection ratio for the communication network using thecollected preamble miss information, determining a target receiver powerand a power ramp-up using the average nose rise and the average misseddetection ratio.

In one example embodiment, the at least a first processor is furtherconfigured to detect the RACH preambles by determining the normalizedRACH detection threshold by performing the steps of, collecting preamblefalse alarm ratio information, computing an average false alarm rate forthe communication network based on the preamble false alarm ratioinformation, and determining the normalized RACH detection thresholdbased on the average false alarm rate, wherein the at least a firstprocessor is further configured to control the network traffic by,broadcasting the normalized RACH detection threshold to UEs of thecommunication network.

In one example embodiment, the at least a first processor is furtherconfigured to, collect a contention ratio information, compute anaverage contention ratio for the communication network based on thecollected contention ratio information, determine the opportunity periodbased on the average contention ratio, wherein the at least a firstprocessor is further configured to control the network traffic by,broadcasting the opportunity period to UEs of the communication network.

In one example embodiment, the at least a first processor is furtherconfigured to, repeat the steps of collecting noise rise information andpreamble miss information, and repeating the steps of computing anoverall average noise rise and an average missed detection ratio untilthe determined target receiver power and the power ramp-up remainconstant, wherein the at least a first processor is further configuredto control the network traffic by, broadcasting the target receiverpower and a power ramp-up to UEs of the communication network, whereinthe at least a first processor is further configured to detect the RACHpreambles by determining the normalized RACH detection threshold, oncethe determined target receiver power and the power ramp-up remainconstant, by performing the steps of, collecting preamble false alarmratio information, computing an average false alarm rate for thecommunication network based on the preamble false alarm ratioinformation, and determining the normalized RACH detection thresholdbased on the average false alarm rate, the at least a first processor isfurther configured to control the network traffic by, broadcasting thenormalized RACH detection threshold to UEs of the communication network.

In one example embodiment, the at least a first processor is furtherconfigured to, repeat the steps of collecting preamble false alarm ratioinformation and computing an average false alarm rate until thedetermined normalized RACH detection threshold remains constant, whereinthe at least a first processor is further configured to detect the RACHpreambles by determining an opportunity period, once the determinedtarget receiver power, the power ramp-up, and the normalized RACHdetection threshold remain constant, by performing the steps of,collecting a contention ratio information, computing an averagecontention ratio for the communication network based on the collectedcontention ratio information, determining the opportunity period basedon the average contention ratio, wherein the at least a first processoris further configured to control the network traffic by, broadcastingthe opportunity period to UEs of the communication network.

In one example embodiment, wherein the at least a first processorincludes a plurality of processors, each of the plurality of theprocessors being associated with a respective sector within thecommunication network, the at least a first processor is furtherconfigured to detect the RACH preambles by, each of the processorscollecting noise rise information and preamble miss information for aset of transmitted RACH preambles for the respective sector, each of theprocessors computing an overall average noise rise based on thecollected noise rise information for the respective sector, each of theprocessors computing an average missed detection ratio, for therespective sectors, using the collected preamble miss information fromeach respective sector, each of the processors sharing the overallaverage noise rise and the average missed detection ratio with at leastone of the other processors, of the plurality of processors, which is aneighbor processor, each of the processors determining a target receiverpower and a power ramp-up, for the respective sectors, using the sharedaverage nose rise and the shared average missed detection ratio.

In one example embodiment, wherein the at least a first processor isfurther configured to, repeat the steps of each of the processors of therespective sectors collecting noise rise information and preamble missinformation, and repeating the step of computing an overall averagenoise rise and an average missed detection ratio until the determinedtarget receiver power and the power ramp-up remain constant, wherein theat least a first processor is further configured to control the networktraffic by, each of the processors broadcasting the target receiverpower and a power ramp-up to UEs for the respective sectors, and whereinthe at least a first processor is further configured to detect the RACHpreambles by determining the normalized RACH detection threshold, oncethe determined target receiver power and the power ramp-up remainconstant, by performing the steps of, each of the processors collectingpreamble false alarm ratio information for the respective sectors, eachof the processor computing an average false alarm rate, for therespective sectors, based on the preamble false alarm ratio information,and each of the processors determining the normalized RACH detectionthreshold, for the respective sectors, based on the average false alarmrate, wherein the at least a first processor is further configured tocontrol the network traffic by each of the processors broadcasting thenormalized RACH detection threshold to UEs for the respective sectors.

In one example embodiment, wherein the at least a first processor isfurther configured to, repeat the steps of each of the processors of therespective sectors collecting preamble false alarm ratio information andcomputing an average false alarm rate, until the determined normalizedRACH detection threshold remains constant, wherein the at least a firstprocessor is further configured to detect the RACH preambles bydetermining the opportunity period, once the determined normalized RACHdetection threshold remains constant, by performing the steps of, eachof the processors collecting a contention ratio information for therespective sectors, computing an average contention ratio for therespective sectors based on the collected contention ratio information,each of the processors determining the opportunity period, for therespective sectors, based on the average contention ratio; and whereinthe at least a first processor is further configured to control thenetwork traffic by each of the processors broadcasting the targetreceiver power, the power ramp-up, and the opportunity period to the UEsfor the respective sectors.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features and advantages of example embodiments willbecome more apparent by describing in detail, example embodiments withreference to the attached drawings. The accompanying drawings areintended to depict example embodiments and should not be interpreted tolimit the intended scope of the claims. The accompanying drawings arenot to be considered as drawn to scale unless explicitly noted.

FIG. 1 illustrates a conventional 3rd Generation Partnership ProjectLong-Term Evolution (3GPP LTE) network;

FIG. 2 illustrates a conventional E-UTRAN Node B (eNB);

FIG. 3 illustrates a wireless interface within a reconfigured eNB, inaccordance with an example embodiment;

FIG. 4 illustrates a reconfigured eNB, in accordance with an exampleembodiment;

FIG. 5 illustrates a reconfigured 3GPP LTE network, in accordance withan example embodiment;

FIG. 6 illustrates a random access channel (RACH) signature detectionmethod, in accordance with an example embodiment;

FIG. 7 illustrates a probability of a missed detection using the RACHsignature detection, in accordance with an example embodiment;

FIG. 8 illustrates a probability of a false alarm detection using theRACH signature detection, in accordance with an example embodiment;

FIG. 9 illustrates an average number of trials using the RACH signaturedetection, in accordance with an example embodiment;

FIG. 10 illustrates a centralized LTE architecture, in accordance withan example embodiment;

FIG. 11A illustrates a method optimizing power, in accordance with anexample embodiment;

FIG. 11B illustrates a method optimizing power, in accordance with anexample embodiment;

FIG. 11C illustrates a method optimizing power, in accordance with anexample embodiment;

FIG. 12 illustrates a scaling function used in the method of optimizingpower, in accordance with an example embodiment;

FIG. 13 illustrates an evolution of the PRACH target receiver powerusing the method of optimizing power, in accordance with an exampleembodiment;

FIG. 14 illustrates an evolution of the probability of missed detectionusing the method of optimizing power, in accordance with an exampleembodiment;

FIG. 15 illustrates an evolution of the probability of a false alarmusing the method of optimizing power, in accordance with an exampleembodiment;

FIG. 16 illustrates an evolution of the average number of trials usingthe method of optimizing power, in accordance with an exampleembodiment;

FIG. 17 illustrates a relationship between the normalized threshold andthe logarithm probability of a false alarm, in accordance with anexample embodiment:

FIG. 18 illustrates a method for optimizing a threshold value, inaccordance with an example embodiment;

FIG. 19 illustrates a relationship between RACH intensity and RACHopportunity period, in accordance with an example embodiment;

FIG. 20 illustrates relationships between RACH preamble collisionprobabilities and RACH intensities for different RACH opportunityperiods T_(RACH), in accordance with an example embodiment;

FIG. 21 illustrates a method of optimizing a periodicity, in accordancewith an example embodiment;

FIG. 22 illustrates a comprehensive RACH parameter optimization, inaccordance with an example embodiment:

FIG. 23 illustrates a decentralized LTE architecture, in accordance withan example embodiment;

FIG. 24A illustrates a decentralized optimization of power, inaccordance with an example embodiment;

FIG. 24B illustrates a decentralized optimization of power, inaccordance with an example embodiment:

FIG. 24C illustrates a decentralized optimization of power, inaccordance with an example embodiment;

FIG. 25 illustrates a decentralized comprehensive RACH parameteroptimization, in accordance with an example embodiment;

FIG. 26 illustrates a communication diagram with exchange of messagesbetween a user equipment and an eNB for the RACH procedure, inaccordance with an example embodiment; and

FIG. 27 illustrates another communication diagram with exchange ofmessages between a user equipment and an eNB for the RACH procedure, inaccordance with an example embodiment.

DETAILED DESCRIPTION

While example embodiments are capable of various modifications andalternative forms, embodiments thereof are shown by way of example inthe drawings and will herein be described in detail. It should beunderstood, however, that there is no intent to limit exampleembodiments to the particular forms disclosed, but on the contrary,example embodiments are to cover all modifications, equivalents, andalternatives falling within the scope of the claims. Like numbers referto like elements throughout the description of the figures.

Before discussing example embodiments in more detail, it is noted thatsome example embodiments are described as processes or methods depictedas flowcharts. Although the flowcharts describe the operations assequential processes, many of the operations may be performed inparallel, concurrently or simultaneously. In addition, the order ofoperations may be re-arranged. The processes may be terminated whentheir operations are completed, but may also have additional steps notincluded in the figure. The processes may correspond to methods,functions, procedures, subroutines, subprograms, etc.

Methods discussed below, some of which are illustrated by the flowcharts, may be implemented by hardware, software, firmware, middleware,microcode, hardware description languages, or any combination thereof.When implemented in software, firmware, middleware or microcode, theprogram code or code segments to perform the necessary tasks may bestored in a machine or computer readable medium such as a storagemedium, such as a non-transitory storage medium. A processor(s) mayperform the necessary tasks.

Specific structural and functional details disclosed herein are merelyrepresentative for purposes of describing example embodiments. Thisinvention may, however, be embodied in many alternate forms and shouldnot be construed as limited to only the embodiments set forth herein.

It will be understood that, although the terms first, second. etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first element could be termed asecond element, and, similarly, a second element could be termed a firstelement, without departing from the scope of example embodiments. Asused herein, the term “and/or” includes any and all combinations of oneor more of the associated listed items.

It will be understood that when an element is referred to as being“connected” or “coupled” to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected” or “directly coupled” to another element, there are nointervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between” versus “directly between,” “adjacent” versus “directlyadjacent.” etc.).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments. As used herein, the singular forms “a.” “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises,” “comprising.” “includes” and/or “including,” when usedherein, specify the presence of stated features, integers, steps,operations, elements and/or components, but do not preclude the presenceor addition of one or more other features, integers, steps, operations,elements, components and/or groups thereof.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedconcurrently or may sometimes be executed in the reverse order,depending upon the functionality/acts involved.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which example embodiments belong. Itwill be further understood that terms, e.g., those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

Portions of the example embodiments and corresponding detaileddescription are presented in terms of software, or algorithms andsymbolic representations of operation on data bits within a computermemory. These descriptions and representations are the ones by whichthose of ordinary skill in the art effectively convey the substance oftheir work to others of ordinary skill in the art. An algorithm, as theterm is used here, and as it is used generally, is conceived to be aself-consistent sequence of steps leading to a desired result. The stepsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofoptical, electrical, or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

In the following description, illustrative embodiments will be describedwith reference to acts and symbolic representations of operations (e.g.,in the form of flowcharts) that may be implemented as program modules orfunctional processes include routines, programs, objects, components,data structures, etc., that perform particular tasks or implementparticular abstract data types and may be implemented using existinghardware at existing network elements. Such existing hardware mayinclude one or more Central Processing Units (CPUs), digital signalprocessors (DSPs), application-specific-integrated-circuits, fieldprogrammable gate arrays (FPGAs) computers or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, or as is apparent from the discussion,terms such as “processing” or “computing” or “calculating” or“determining” of “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical, electronicquantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storage,transmission or display devices.

Note also that the software implemented aspects of the exampleembodiments are typically encoded on some form of program storage mediumor implemented over some type of transmission medium. The programstorage medium may be any non-transitory storage medium such as magnetic(e.g., a floppy disk or a hard drive) or optical (e.g., a compact diskread only memory, or “CD ROM”), and may be read only or random access.Similarly, the transmission medium may be twisted wire pairs, coaxialcable, optical fiber, or some other suitable transmission medium knownto the art. The example embodiments not limited by these aspects of anygiven implementation.

General Methodology:

The achievement of optimal random access performance through efficientRACH signature detection algorithms and the correct configuration of theRACH parameters, which may be adapted to diverse situations encounteredin practical deployment of cellular networks, may meet at least thefollowing five objectives.

A) Avoidance of excessive interference on communication channels;

B) Achievement of an intended RACH coverage;

C) Minimization of delays related to call setup, handovers, etc.;

D) Reduction of network signaling overhead related to identificationassignments, resource grants, etc.; and

E) Obtainment of a proper resource allocation balance between randomaccess and other communication needs.

The five objectives, listed above, may be quantified in terms ofmetrics, and these metrics are referred to as ‘network metrics’throughout the remainder of this document. Due to themulti-dimensionality of network problems associated with thesimultaneous optimization of several parameters, and due to the dynamicnature of cellular networks (with varying traffic loads and changinginterference patterns), automated and adaptive RACH parameter settingmethods may assist proper network operations. More specifically, some orall of the following parameters may be optimized via the exampleembodiment. Throughout the remainder of this document, these parametersare referred to as ‘RACH parameters.’

1) RACH initial target receive power P_(0,RACH);

2) RACH transmit power ramp-up P_(ramp,RACH);

3) Normalized RACH preamble detection threshold T_(RACH);

4) RACH resource allocation period τ_(RACH).

Within the context of the example embodiments, the optimization of RACHparameters 1) and 2) may directly affect objectives A), B). C); theoptimization of RACH parameter 3) may directly affect objectives C) andD); and the optimization of RACH parameter 4) may directly affectobjectives A), C) and E). For this reason, example embodiments providean adaptive RACH parameter optimization solution that may be designed tohandle performance constraints which are concurrently affected by theRACH parameters 1) through 4).

Structural Embodiment

FIG. 3 illustrates a wireless interface 220 a that may be included in areconfigured eNB 105 a (see FIG. 4). The wireless interface 220 a mayinclude multiple antenna signal processing chains (101 a through 101 n).While multiple chains 101 may be included, for simplicity only a singlechain 101 a will be described here, for brevity sake. As shown in FIG.3, the interface 220 a may include a receiver antenna 102 a thatreceives data and information that is sent through the chain 101 a. Thechain 101 a may include a cyclic prefix remover 103 a, and a discreteFourier transformation (DFT) 123 a with length N_DFT. The value of N_DFTmay depend on a sampling rate of the system (for example, a system with20 MHz bandwidth may include a value of N_DFT that is equal to 24576).The chain 101 a may also include a subcarrier selector 107 a, asymbol-by-symbol complex multiplier 109 a and a zero-padder 111 a thatperforms zero-padding (i.e., the padder 111 a may insert a sequence ofzeros at an end of the sequence symbols being processed, such that atotal length of the signal may fit the IFFT with a length N_cor). Aninverse fast Fourier transformation (IFFT) 113 a may perform an inversefast Fourier transformation (IFFT) with length N_cor. The value N_cormay be greater than a length of the RACH preamble in symbols (i.e. 839).Typically, N_cor may be set to 1024. A symbol-by-symbol computator 115 amay perform a symbol-by-symbol computation of the squared absolute valueof each symbol. An adder 117 may perform a symbol-by-symbol addition ofthe symbols computed by each antenna signal processing chain 101. Asignature detection block 119 may then perform signature detection basedon the random variable Y (leaving the adder 117). That is to say, thesignature detection block 119 may search and classify the peaks in thecorrelation power profile represented by Y. A generator 131 may generatea prime-length Zadoff-Chu root sequence according to 3GPP TechnicalSpecification 36.211, ‘Physical Channels and Modulation’. A discreteFourier transformer 133 may perform a discrete Fourier transform (DFT)with length N_ZC on complex symbols generated by the sequencer 131.Typically, a length N_ZC may be equal to a value 839. A conjugator 135may perform a symbol-by-symbol complex conjugation operation of thesymbols leaving the discrete Fourier transformer 133. The signals 121leaving the interface 220 a may be signals corresponding to thesignatures detected at the eNB 105 a.

FIG. 4 illustrates a reconfigured eNB 105 a that includes the wirelessinterface 220 a of FIG. 3 (described above). The eNB 105 a includes aRACH Detection Routine (RDR) 300 that may include the algorithms andmethod step information that the processor 210 may run and/or perform inorder to perform the methods illustrated in FIGS. 6, 11A-11C, 18, 21-22,24A-24C, and 25-26 (see the description below). The processor 210 alsocontrols the function of the components of interface 220 a shown in FIG.3.

FIG. 5 illustrates a reconfigured network 10 a that includes thereconfigured eNB 105 a (shown in FIG. 4).

RACH Signature Detection Method:

Relevant to the characterization of the RACH signature detectionalgorithm is a random variable Y (transmitted from adder 117 of FIG. 3),which may be a vector of length N_(cor), i.e., Y=(Y₁, Y₂, . . . , Y_(N)_(cor) ), described as follows.

$\begin{matrix}{{Y_{j} = {\frac{1}{2N_{RX}}{\sum\limits_{k = 1}^{N_{RX}}{X_{k,j}}^{2}}}},{1 \leq j \leq N_{cor}},} & {{Equation}\mspace{14mu} 1}\end{matrix}$

Where N_(RX) may be a number of receiver antennas at the eNB 105 a andX_(k) may be assumed to be a complex, zero-mean with variance σ².

Using Equation 1, the distribution of the random variable represented byeach Y_(j) may be approximated by a chi-squared distribution with2N_(RX) degrees of freedom. After normalization (by making σ²=1), thecumulative distribution function (CDF) may be written as shown below.

$\begin{matrix}{{Q_{x^{2},{2N_{RX}}}(y)} = {1 - {{\exp ( {{- N_{RX}}y} )} \cdot {\sum\limits_{k = 0}^{N_{{RX} - 1}}\frac{( {N_{RX}y} )^{k}}{k!}}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

Assuming that M is a number of metrics to be searched in the correlationpower profile represented by Y, a probability that all M metrics arebelow a normalized threshold value T_(RACH) may be given by [Q_(x) ₂_(,2N) _(RX) (T_(RACH))]^(M). Therefore, the probability that one ormore metrics are above T_(RACH) may be defined as probability of falsealarm P_(FA), as shown below.

P _(FA)=1−[Q _(x) ₂ _(,2N) _(RX) (T _(RACH))]^(M)  Equation 3

By setting P_(FA) to a particular value, T_(RACH) may be determinednumerically using Equations 2 and 3. As an example, Table 1 shows thenormalized threshold values T_(RACH) for M=13·64=832 and severalcombinations of N_(RX) and P_(FA) values.

TABLE 1 P_(FA) 0.01 0.01 0.01 0.001 0.001 0.001 N_(RX) 1 2 4 1 2 4T_(RACH) 13.63 8.24 5.28 15.93 9.46 5.94

In a conventional implementation of a signature detection procedure, anormalized threshold T_(RACH) generally needs to be scaled by a noisefloor of the correlation power profile Y in order to provide an actualthreshold value T′_(RACH) to be used in the detection. However, becauseof the non-stationary nature of Y, due to random RACH transmissions,unpredictable scheduling decisions, and non-Gaussian interference,estimating the noise floor may be difficult, and may lead to inaccurateresults for T′_(RACH). Therefore, use of a detection algorithm which isblind with respect to the noise floor, may be advantageous. Below is analgorithm (Algorithm 1) that describes signature detection based on aforward consecutive mean excision (FCME) method. Inputs for thisalgorithm include a correlation power profile Y=(Y₁, Y₂, . . . , Y_(N)_(cor) ), a normalized RACH detection threshold (T_(RACH)), and aninitial size of a set that does not contain outliers (I).

Algorithm 1—RACH Signature Detection Based on FCME:

FIG. 6 illustrates a random access channel (RACH) signature detectionmethod, in accordance with an example embodiment. As shown in step S300,the processor 210 may cause the signature detection block 119 to sortthe power profile values Y_(j) in ascending order, i.e., {Y₁, Y₂, . . ., Y_(N) _(cor) }={Y′₁, Y′₂, . . . , Y′_(N) _(cor) } with Y′₁≤Y′₂≤ . . .≤Y′_(N) _(cor) , where

=∅ and, stopFlag=0, and k=I, while (stopFlag=0)∧(k≤N_(cor)). In stepS302, the processor 210 may then compute a weighting factor T_(k) to beused with the values {Y′₁, Y′₂, . . . , Y′_(N) _(cor) }, where theweighting factor may be defined as T_(k)=T_(RACH)/k.

In step S304, the processor 210 may determine if Y′_(k+1)>T_(k)Σ_(i=1)^(k)Y′_(i). If this relationship holds true, then in step S306 theprocessor 210 may determine outliers {Y′_(k+1), Y′_(k+2), . . . , Y′_(N)_(cor) } corresponding to the transmitted signatures, such that

={Y′_(k+1), Y′_(k+2), . . . , Y′_(N) _(cor) }. Based on a determinationof the outliers, in step S308 the processor 210 may then remap elementsof

to the original metric space (i.e., signature windows), whereupon theprocedure may stop at step S310.

If in step S304 the processor 210 determines that the relationshipY′_(k+1)>T_(k)Σ_(i=1) ^(k)Y′_(i) does not hold true, then in step S312the processor 210 may increment k (k=k+1) in step S312, whereupon theprocessor may determine if k is less than or equal to Ncor (in stepS314), and if k is less than or equal to Ncor then the processor repeatsstep S302 (otherwise the procedure ends at step S316).

FIGS. 7-9 depict simulation results related to the performance of themethod of FIG. 6. Specifically, FIG. 7 illustrates a probability of amissed detection for an overall network as a function of a targetreceive power that may be set for random access communications. As shownin this figured, forward consecutive mean excision (FCME) method of FIG.6, and the threshold scaling (i.e., conventional method) showapproximately a same performance with power ramp-ups (PRamp) having someminor impact only for lower target receive powers.

FIG. 8 depicts a probability of a false alarm as a function of a targetreceive power level for a UE 110 transmitting a RACH preamble. As shownin FIG. 8, the method of FIG. 6 has clear advantages over theconventional threshold scaling. FIG. 9 depicts an average number oftrials as a function of a target receive power level for a UE 110transmitting a RACH preamble. As shown in FIG. 9, the FCME and thresholdscaling show similar performance, although PRamp has some impact on anumber of attempts for lower target receiver powers.

Optimization of RACH Initial Target Receive Power P_(0,RACH) and PowerRamp-Up P_(ramp,RACH):

Following an initial cell synchronization process, the UE 110 may decodeuseful information related to cell access. Specifically, the SystemInformation Block 2 (SIB2, in step S900 of FIG. 26) may broadcast to allUEs 110, and this broadcast may carry information related to RACHparameters, which may include a RACH initial target receive powerP_(0,RACH) and a power ramp-up P_(ramp,RACH). Upon decoding of the RACHparameters, the UEs 110 may adjust a transmit power of the RACHpreambles P_(RACH) as shown below.

P _(RACH)(k)=min{P _(max) ,P _(0,RACH) −P _(loss)+(k−1)P_(ramp,RACH)+Δ_(preamble)},   Equation 4

Where P_(max) may be a maximum power that can be transmitted by the UE110, P_(loss) may be a path-loss that is to be compensated for(estimated using downlink reference signals), k may be a number ofattempts associated with the preamble detection by the eNB 105 a, andΔ_(preamble) may be a power offset that may depend on a type of RACHpreamble being transmitted.

As mentioned above, the power level defined by P_(0,RACH) andP_(ramp,RACH) may directly reflect the network metrics A)-C). In thiscontext, the metrics A)-C) may be periodically monitored such that aproper adjustment of P_(0,RACH) and P_(ramp,RACH) may be made.

In the case of network metric A), P_(0,RACH) and P_(ramp,RACH) mayimpact network interference levels in two ways.

First, the Physical Random Access Channel (PRACH), which has a transmitpower controlled by P_(0,RACH) and P_(ramp,RACH), and which transportsthe RACH preambles, may cause interference in data/control channels(PUSCH/PUCCH) of neighboring sectors.

Second, after the RACH preamble (Message 1 in step S902 of FIG. 26) froma particular UE 110 is detected, the eNB 105 a may acknowledge (Message2 in step S904 of FIG. 26). Then, the UE 110 may respond to the eNB 105a (Message 3 in step S906 of FIG. 26), which may usually be sent on theuplink data channel PUSCH, with power settings derived from asuccessfully detected Message 1. The portions of the PUSCH carryingMessage 3 may cause interference in the data/control channels ofneighboring sectors. The intensity of the Message 3 interference maythen be a function of P_(0,RACH) and the accumulation of power ramp-upsP_(ramp,RACH).

In order to measure network metric A) such that P_(0,RACH) andP_(ramp,RACH) may be controlled, the eNB 105 a measurements associatedwith a received interference power and a thermal noise power may beused, as shown below.

$\begin{matrix}{{I_{v,t}^{{msg}\; 1}(p)} = {\sum\limits_{u \neq v}{I_{u,v,t}^{{msg}\; 1}(p)}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

Equation 5 describes an interference caused by the messages 1 (step S902of FIG. 26) on the physical resource block p of sector v at time t. Notethat I_(v,t) ^(msg1)(p) may be a sum of the terms I_(u,v,t) ^(msg1)(p)(i.e., an interference due to the message 1 originating from sector u onthe physical resource block p of sector v at time t. Similarly, theinterference caused by message 3 on the physical resource block p ofsector v at time t is shown in Equation 6.

$\begin{matrix}{{I_{v,t}^{{msg}\; 3}(p)} = {\sum\limits_{u \neq v}{I_{u,v,t}^{{msg}\; 3}(p)}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

A thermal noise on the physical resource block p of sector v at time tmay be defined as N_(v,t)(p). In the case of receiver diversity, allquantities describe above may represent a linear average of themeasurements in each diversity branch.

Based on the quantities described above, a single parameter may be usedto represent the network metric A). This parameter may be designated asa noise rise, R_(v,t)(p), and may be defined as follows.

$\begin{matrix}{{R_{v,t}(p)} = \frac{{I_{v,t}^{{msg}\; 1}(p)} + {I_{v,t}^{{msg}\; 3}(p)} + {N_{v,t}(p)}}{N_{v,t}(p)}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

Where this variable relates to the physical resource block p of sector vat a time t. Note that either the component I_(u,v,t) ^(msg1)(p) or thecomponent I_(u,v,t) ^(msg3)(p) from a particular originating sector u,or none of these components, may be contributing to the noise floorR_(v,t)(p). The contribution of each interference component may bedependent upon a relative alignment of the physical resource blocksacross the multiple sectors of the network.

In order to measure the network metrics B) and C), the preamble missratio for the l-th trial in sector v at time t, M_(v,t)(l) may bequantified. M_(v,t)(l) may be defined as follow.

$\begin{matrix}{{M_{v,t}(l)} = {1 - \frac{D_{v,t}(l)}{S_{v,t}(l)}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$

Where D_(v,t)(l) may be a number of preambles validated with messages 3during an l-th trial in sector v at time t, and S_(v,t)(l) may be atotal number of preambles sent for the l-th attempt in sector v at timet. In both counters D_(v,t)(l) and S_(v,t)(l), the preambles detected asa result of the contention resolution process may be excluded.Furthermore, from an implementation perspective, the counters D_(v,t)(l)and S_(v,t)(l) may be obtained from the information received as aresponse to the UEInformationRequest message sent by the eNB's 105 a toa UE (see FIG. 27).

The values in Equations 7 and 8 may be defined for a certain instant oftime t. However, for system optimization purposes, an averaging value ofR_(v,t)(p) and M_(v,t)(t) over a period of time is useful. This is shownin the equations below.

$\begin{matrix}{{{R_{v,{\lbrack{kT}\rbrack}}(p)} = {\frac{1}{T}{\sum\limits_{t = {{({k - 1})}T}}^{kT}{R_{v,t}(p)}}}},} & {{Equation}\mspace{14mu} 9} \\{{{M_{v,{\lbrack{kT}\rbrack}}(l)} = {1 - \frac{\sum\limits_{t = {{({k - 1})}T}}^{kT}{D_{v,t}(l)}}{\sum\limits_{t = {{({k - 1})}T}}^{kT}{S_{v,t}(l)}}}},} & {{Equation}\mspace{14mu} 10}\end{matrix}$

Where T may be defined as a sampling period during which measurementsmay be made.

Using quantities described in Equations 9 and 10, the RACH parametersP_(0,RACH) and P_(ramp,RACH) may be obtained from the multi-objectiveoptimization program described below.

$\begin{matrix}{{\arg {\max\limits_{{P_{0,{RACH}}{(k)}},{P_{{ramp},{RACH}}{(k)}}}( {{f( R_{\lbrack{kT}\rbrack} )},{g( M_{\lbrack{kT}\rbrack} )}} )}}{{subject}\mspace{14mu} {to}\text{:}}\begin{matrix}{{{R_{v,{\lbrack{kT}\rbrack}}(p)} \leq R_{\max}},} & {{\forall v},t,p,} \\{{{M_{v,{\lbrack{kT}\rbrack}}(l)} \leq {M_{\max}(l)}},} & {{\forall v},t,}\end{matrix}} & {{Equation}\mspace{14mu} 11}\end{matrix}$

Where R_([kT])=(R_([kT])(1),R_([kT])(2), . . . ) withR_([kT])(p)=(R_(1,[kT])(p), R_(2,[kT])(p), . . . , R_(N) _(sector)_(,[kT])(p)), M_([kT])=(M_([kT])(1), M_([kT])(2), . . . ) withM_([kT])(l)=(M_(1,[kT])(l), M_(2,[kT])(l), . . . , M_(N) _(sector)_(,[kT])(l)), R_(max) may be a maximum tolerable noise rise, M_(max)(l)may be a maximum tolerable preamble miss ratio at the l-th trial, andN_(sector) may be a total number of sectors in the system that may beoptimized. Although more sophisticated cost functions may be considered,in the most simple case the functions ƒ(⋅) and g(⋅) describe an averageof these variables R_(v,[kT])(p) and M_(v,[kT])(l) throughout thesectors v.

Centralized Architecture (Algorithm 2):

FIG. 10 illustrates a centralized architecture involving a centraloffice 500 (or, a central node) in communication with multiple eNBs (105a 1-105 a 6, that are identical to eNB 105 a of FIG. 4) in a geographicarea 501. The central office 500 may include a processor 502, memory504, a wireless interface 506 to communicate with each of the eNBs 105a, and a backhaul 508. A Power Optimization Routine (POR) 510 may beincluded in the memory. The processor 502 may cause the POR 510 toperform the method steps of the method flowchart shown in FIGS. 11A-11C,described below.

Referring to FIG. 10, the architecture may be used to control the RACHparameters (e.g., P_(0,RACH) and P_(ramp,RACH)). To this end, themeasurements such as R_(v,k)(p) and M_(v,k)(k) may be collected withinthe individual sectors v serviced by each respective eNB 105 a. Themeasurements may then be sent via a network communication interface tothe central office 500 to perform the optimization defined in Equation11.

The multi-objective optimization program defined by Equation 11 may besolved using numerical methods such as an evolutionary algorithm whichmay use the concept of Pareto optimality. Alternatively, each element inthe objective vector in Equation 11 may be optimized individually, butin coordination with each other. A specific method of implementing thislatter method is shown in FIGS. 11A-11C.

Algorithm 2—Optimizing Power:

In algorithm 2, a maximum tolerable value for the noise rise andpreamble miss ratios (i.e., R_(max), M_(max,1) and M_(max),respectively) may be set in agreement with each other because of anintertwined effect that variations in P_(0,RACH) and P_(ramp,RACH) maycause. In this context, the scaling constants, γ_(P) and γ_(R), and thescaling functions, γ_(N) ¹ and γ_(N) ₂ , may be selected such that aspeed of convergence and oscillations on network metrics may be kept inbalance. Note that the scaling functions γ_(N) ₁ and γ_(N) ₂ may bedesigned to avoid “ping-pong” situations due to power updates driven bymissed detection ratios and interference levels. The general shape forfunctions γ_(N) ₁ and γ_(N) ₂ are shown in FIG. 12. Moreover, theprecision values ϵ_(M) ₁ , ϵ_(M) ₂ and ϵ_(R) may also be configured toprovide additional stability to the network metrics by avoidingunnecessary changes in RACH parameters. Finally, a function Q{⋅}, whichmay be based on simulations, may be responsible for the mapping ofcontinuous values into the discrete P_(ramp,RACH) values supported bythe network.

FIG. 11A depicts a flowchart of method steps involved in determiningRACH power parameters using a centralized architecture (such as thearchitecture of FIG. 10), using the algorithm 2 approach. Specifically,in step S400, the processor 502 of the CO 500 may set k=1, andx_(γ1)=x_(γ2)=N_(γ), in order to collect measurements R_(v,[kT])(p) andM_(v,[kT])(l) from the N_(sector) sectors in the centralized system(FIG. 10), where this information collection comes from a collection ofprocessors 210 of a number of base stations eNB (105 a 1-105 a 6, whereeach eNB may be identical to eNB 105 a of FIG. 4). In step S402, theprocessor 502 may compute an average noise rise for the network, where acounter p may correspond to a physical resource block where Messages 1and/or 3 (FIG. 26) may cause interference and N_(p) may be a totalnumber of such resource blocks, where P=1 to N_(p) within therelationship R_([kT])(p)=(1/N_(sector))Σ_(v=1) ^(N) ^(sector)R_(v,[kT])(p).

In step S404, the processor 502 may compute an overall average noiserise for the network, as defined by R_([kT])=(1/N_(p))Σ_(p=1) ^(N) ^(p)R_([kT])(p). In step S406, the processor 502 may then Compute an averagemissed detection ratios for the network for l=1 to N_(l) using therelationship M_([kT])(l)=(1/N_(sector))Σ_(v=1) ^(N) ^(sector)M_(v,[kT])(l). In step S408, the processor 502 may compute an averagemissed detection ratio for the second trial (and further trials) usingM_([kT])=(1/(N_(l)−1))Σ_(l=2) ^(N) ^(l) M_([kT])(l).

In step S410, the processor 502 may determine if the relationship if|M_(max,1)−M_([kT])(1)|>ϵ_(M) ₁ holds true, and if so then in step S412the processor 502 may adjust target receiver power based onP_(0,RACH)(k)=P_(0,RACH)(k−1)=γ_(P)(M_(max,1)−M_([kT])(1)), whereM_(1—)Flag(k)=1.

If in step S410 the relationship|M_(max,1)−M_([kT])(1)|>ϵ_(M) ₁ does nothold true, then in step S414 the processor 502 may adjust the targetreceive power by P_(0,RACH)(k)=P_(0,RACH)(k−1), where M_(1—)Flag(k)=0.

Following steps S412 or S414, in step S416 (FIG. 11B) the processor 502may determine if the relationship |M_(max)−M_([kT])|>ϵ_(M) ₂ holds true,and if so then in step S420 the processor 502 may adjust power ramp-upP_(ramp,RACH)(k) byP_(ramp,RACH)(k)=Q_(ramp){P_(ramp,RACH)(k−1)−γ_(R)(M_(max)=M_([kT]))},where M_(2—)Flag(k)=1. Otherwise, the processor 502 may adjust the poweraccording to P_(ramp,RACH)(k)=P_(ramp,RACH)(k−1), where M_(2—)Flag(k)=0in step S418.

Following steps S420 or S422, the processor 502 may make adjustments inP_(0,RACH)(k) or P_(ramp,RACH)(k) to control the noise rise by adjustingR_(1—)Flag(k)=0 and R_(2—)Flag(k)=0.

In step S424, the processor 502 then determines whether the relationshipR_([kT])−R_(max)>ϵ_(R) holds true, and if so the processor 502 thendetermines (in step S426) whether the relationshipM_([kT])(1)−M_(max,1)<ϵ_(M) ₁ also holds true, and if so the processor502 then adjusts the receive power to control a noise increase usingP_(0,RACH)(k)=P_(0,RACH)(k−1)+γ_(N) ₁ (x_(γ) ₁ )·(R_(max)−R_([kT])),where R_(1—)Flag(k)=1 in step S428. Otherwise, if the processor 502determines the determination of step S426 to be negative, then in stepS430 the processor then determines whether the relationshipM_([kT])−M_(max)<ϵ_(M) ₂ holds true (where an affirmative responsecauses the processor 502 to perform the step of S432, and a negativeresponse causes the processor 502 to perform the step of S434). If theprocessor 502 returns a negative response to the inquiry of step S424,then the processor 502 next performs the step of S434.

In step S432 (FIG. 11c ), the processor 502 may adjust a receiver powerto control a noise increase, by computingP_(ramp,RACH)(k)=Q_(ramp){P_(ramp,RACH)(k−1)+γ_(N) ₂ (x_(γ) ₂)·(R_(max)−R_([kT]))} where R_(2—)Flag(k)=1. Then in step S434, theprocessor 210 may compute an intensity of “ping-pong” situations for thelast N_(γ) sampling periods by determining x_(γ) ₁ =(Σ_(i=max(k-N) _(γ)_(,1)) ^(k)M_(1—)Flag(k))−(Σ_(i=max(k-N) _(γ) _(,1)) ^(k)R_(1—)Flag(k))and x_(γ) ₂ =(Σ_(i=max(k-N) _(γ) _(,1))^(k)M_(2—)Flag(k))−(Σ_(i=max(k-N) _(γ) _(,1)) ^(k)R_(2—)Flag(k)).

In step S436, the processor 502 may go to a next sampling period byincrementing k (k=k+1), and then returning to step S400 (FIG. 11A).

FIGS. 13-16 illustrates simulation results associated with determiningpower levels using the centralized architecture of FIG. 10.Specifically. FIGS. 13-16 show an impact of a control algorithm for theRACH target receive power, P_(0,RACH), on the network metrics that areinvestigated. The simulation consists of a two-ring, 120-degree-sectoredcellular network (i.e., 57 sectors) with wraparound, down-tilt,path-loss, shadow fading and uniform user distribution over thegeographical area. The random accesses are assumed to be Poissondistributed with an intensity of 32 preambles/sector/second. The controlalgorithm for P_(0,RACH) consist in the implementation of step S412 withγ_(P)=20, M_(max,1)=0.1 and T=0.1 seconds. Note that other values γ_(P)and M_(max,1) may be chosen to provide better system stability.

FIG. 13 depicts an evolution of P_(0,RACH) over a period of time, wherethe FCME method is shown to perform better than conventional thresholdscaling. FIG. 14 depicts an evolution of the probability of a misseddetection over a period of time. In this case, convergence is achievedfor both signature detection methods at M_(max,1)=0.1. FIG. 15 depictsan evolution of the probability of a false alarm over a period of time.In this drawing, the FCME method provides advantages over a conventionalthreshold scaling method. FIG. 16 depicts an evolution of an averagenumber of trials over time.

Optimization of the Normalized RACH Preamble Detection ThresholdT_(RACH) (Algorithm 3):

An optimization of T_(RACH) may have a direct effect on network metricsC) and D) (i.e., minimization and delays and reduction of networksignaling), respectively. As discussed above, the network metric C) maybe measured in the eNBs 105 a by computing a preamble miss ratio asdefined in Equations 8 and 10. On the other hand, in order to measurenetwork metric D), a new quantity called a ‘preamble false alarm ratio’may be defined. For sector v at time t, preamble false alarm ratioF_(v,t) may be defined as follows.

$\begin{matrix}{{F_{v,t} = {1 - \frac{N_{v,t}^{{msg}\; 3}}{N_{v,t}^{{msg}\; 2}}}},} & {{Equation}\mspace{14mu} 12}\end{matrix}$

Where N_(v,t) ^(msg3) is a number of messages 3 (see FIG. 26) receivedin sector v at time t and N_(v,t) ^(msg2) is a number of messages 2 (seeFIG. 26) sent by sector v at time t. Similar to the other quantitiesdefined above, a time averaged F_(v,t) may also be more convenient, andthis value may be defined as shown below.

$\begin{matrix}{F_{v,{\lbrack{kT}\rbrack}} = {1 - {\frac{\sum\limits_{t = {{({k - 1})}T}}^{kT}N_{v,t}^{{msg}\; 3}}{\sum\limits_{t = {{({k - 1})}T}}^{kT}N_{v,t}^{{msg}\; 2}}.}}} & {{Equation}\mspace{14mu} 13}\end{matrix}$

An optimization of T_(RACH) may also be defined by an optimizationprogram similar to Equation 11. Equation 14 (below) offers such anoptimization.

$\begin{matrix}{{\arg {\max\limits_{T_{RACH}}{f( F_{\lbrack{kT}\rbrack} )}}}{{subject}\mspace{14mu} {to}\text{:}}\begin{matrix}{{F_{v,{\lbrack{kT}\rbrack}} \leq F_{\max}},} & {{\forall v},t,}\end{matrix}} & {{Equation}\mspace{14mu} 14}\end{matrix}$

Where F_([kT])=(F_(1,[kT]), F_(2,[kT]), . . . , F_(N) _(sector)_(,[kT])), F_(max) may be a maximum tolerable false alarm rate, andN_(sector) may be the total number of sectors in the system that may beoptimized. Although more sophisticated cost functions may be considered,in the simplest case the function ƒ(⋅) describes an average of thevariable F_(v,[kT]) throughout the sectors v.

In order to solve the problem defined by Equation 14, the systemarchitecture depicted by FIG. 10 may be employed together with gradientbased optimization methods. In this architecture, a central office (CO500) may perform the method steps described below by running a poweroptimization routine (POR 510) saved to memory that includes thealgorithms to perform the method steps. Alternatively, the dependencybetween T_(RACH) and P_(FA) as given in Equation 3 may be used toprovide a simpler method to find an optimum value for T_(RACH). In thiscontext, FIG. 17 depicts a relationship between T_(RACH) andlog₁₀(P_(FA)) for M=832 and N_(RX)={1, 2, 4} Relationships betweenT_(RACH) and log₁₀(P_(FA)) are approximately linear for a wide range ofP_(FA) values. The slopes α_(FA) for each N_(RX) value are summarized inTable 2 (shown below).

TABLE 2 N_(RX) 1 2 4 α_(FA) −2.31 −1.22 −0.66

Thus, using the slopes provided in Table 2, a new normalized threshold{tilde over (T)}_(RACH) from the current threshold {circumflex over(T)}_(RACH) may be computed such that a target probability of falsealarm P_(FA,target) may be achieved from a current probability of falsealarm P _(FA). To this end, the following equation may define theserelationships.

T _(RACH)=α_(FA)·[log₁₀(P _(FA,target))−log₁₀({circumflex over (P)}_(FA))]+{circumflex over (T)} _(RACH)  Equation 15

Algorithm 3—Optimization of the Preamble Detection Threshold:

Optimization of the normalized RACH preamble detection threshold(described above) is reflected in the method shown in FIG. 18. As shownin step S500 of FIG. 18, the processor 502 of CO 500 may set k=1, andthen the processor 502 may collect measurements F_(v,[kT]) from a numberof base stations (eNB 105 a 1-105 a 6) from geographic area 501 forfurther processing. It is important to note that in each eNB 105 a ofthe area 501, a processor 210 for the respective eNB 105 may compute themeasurement F_(v,[kT]) based on Equations 12 and 13, where thisinformation may be stored in memory 225 of the eNB 105 a until theprocessor 500 requests this stored information. Then, in step S502, theprocessor 502 may compute an average false alarm rate for the network bydetermining F_([kT])=(1/N_(sector))Σ_(v=1) ^(N) ^(sector) F_(v,[kT]).

In step S504, the processor 502 may determine whether the relationship|F_(max)−F_([kT])|>ϵ_(FA) holds true, and if so then in step S508 theprocessor 502 may update the threshold byT_(RACH)(k)=α_(FA)·[log₁₀(F_(max))−log₁₀(F_([kT]))]+T_(RACH)(k−1).Otherwise, the processor 502 may determine thatT_(RACH)(k)=T_(RACH)(k−1) in step S506. Following steps S506 or S508,the processor 502 may increment k (k=k+1) in step S510, and return tostep S500 to repeat the algorithm 2 procedure.

Optimization of RACH Opportunity Period τ_(RACH) (Algorithm 4):

By controlling the RACH opportunity period τ_(RACH) (i.e., a frequencywith which PRACH opportunity slots may be provided for random accesscommunications), an optimization of network metric E) may be achieved.In this context, a measurement may be provided by the eNBs 105 a thatindicates a suitability of network metric E), where the measurement maybe a ‘contention ratio.’ The contention ratio, C_(v,t), for sector v attime t may be defined as follows.

$\begin{matrix}{C_{v,t} = {1 - \frac{A_{v,t}}{D_{v,t}}}} & {{Equation}\mspace{14mu} 16}\end{matrix}$

Where D_(v,t) may be an overall number of preambles detected in sector vat time t, and A_(v,t) may be a total number of UEs 110 that weregranted access to the network without the need for the ‘contentionresolution’ procedure.

Similar to the other network measurements defined above, systemoptimization purposes dictate the use of a time averaging of C_(v,t), asshown in the equation below.

$\begin{matrix}{C_{v,{\lbrack{kT}\rbrack}} = {1 - \frac{\sum\limits_{t = {{({k - 1})}T}}^{kT}A_{v,t}}{\sum\limits_{t = {{({k - 1})}T}}^{kT}D_{v,t}}}} & {{Equation}\mspace{14mu} 17}\end{matrix}$

Where T may be defined as a sampling period during which measurementsmay be collected.

An optimization program may be defined to find a optimum value forτ_(RACH), as shown below.

$\begin{matrix}{{\arg {\max\limits_{T_{RACH}}{f( C_{\lbrack{kT}\rbrack} )}}}{{subject}\mspace{14mu} {to}\text{:}}\begin{matrix}{{C_{v,{\lbrack{kT}\rbrack}} \leq C_{\max}},} & {{\forall v},t,}\end{matrix}} & {{Equation}\mspace{14mu} 18}\end{matrix}$

Where C_([kT])=(C_(1,[kT]), C_(2,[kT]), . . . , C_(N) _(sector)_(,[kT])), C_(max) may be a maximum tolerable contention ratio,N_(sector) may be a total number of sectors in the system that may beoptimized, and ƒ(⋅) may describe an average of the variable C_(v,[kT])throughout the sectors v.

Similar to the normalized threshold T_(RACH), a simpler means of solvingthe problem in Equation 18 may be implemented, where it is assumed thatan exponential distribution with parameter λ for inter-arrival timeexists between each pair of consecutive RACH preamble transmissions. Thenumber of RACH preamble transmissions, k, in any given time span, τ, maybe distributed according to a Poisson distribution, as shown below.

$\begin{matrix}{{P\{ k \}} = \frac{{e^{- {\lambda\tau}}({\lambda\tau})}^{k}}{k!}} & {{Equation}\mspace{14mu} 19}\end{matrix}$

From Equation 19, a collision probability, p_(col), as seen by aparticular UE 110 during a time span τ may be written as follows.

$\begin{matrix}{p_{col} = {{\sum\limits_{k = 1}^{\infty}\frac{{e^{- {\lambda\tau}}({\lambda\tau})}^{k}}{k!}} = {{e^{- {\lambda\tau}}( {e^{- {\lambda\tau}} - 1} )} = {1 - e^{- {\lambda\tau}}}}}} & {{Equation}\mspace{14mu} 20}\end{matrix}$

Thus, for a particular p_(col) and a time span τ, a supported intensityλ may be determined as follows.

$\begin{matrix}{\lambda = {- \frac{\log ( {1 - p_{col}} )}{\tau}}} & {{Equation}\mspace{14mu} 21}\end{matrix}$

Because there are N_(preamble) orthogonal preambles available in eachsector, for a particular p_(col) and a particular RACH opportunityperiod τ_(RACH), a supported RACH intensity λ_(RACH) may be determinedas follows.

$\begin{matrix}{\lambda_{RACH} = {{- N_{preamble}} \cdot \frac{\log ( {1 - p_{col}} )}{\tau_{RACH}}}} & {{Equation}\mspace{14mu} 22}\end{matrix}$

Algorithm 4—Optimizing Periodity:

FIG. 19 depicts a supported RACH intensity as a function of a RACHopportunity period (for 64 preambles per sector), where RACH intensitiesλ_(RACH) varies with RACH opportunity periods τ_(RACH) forN_(preamble)=64 and p_(col)={0.001, 0.01, 0.1}. FIG. 20 depictscollision probabilities p_(col) as a function of RACH intensitiesλ_(RACH) for N_(preamble)=64 and τ_(RACH)={1, 2, 5, 10, 15, 20} ms.Based on FIG. 20, a look-up table may be constructed that maps each pair(τ′_(RACH), p′_(col)), with p′_(col)≤p_(max) and τ′_(RACH) greater thana maximum tolerable probability p_(max), to a new pair (τ′_(RACH),p′_(col)), where p′_(col)≤p_(max) and τ′_(RACH) may be a largestpossible RACH opportunity period. A mapping example for (τ_(RACH)=20 ms,p_(col)=˜0.06) to (τ′_(RACH)=2 ms, p′_(col)=˜0.006) is also depicted inFIG. 20 for λ_(RACH)=200 preambles/sector/second.

FIG. 21 is a method flowchart providing a method for optimizingτ_(RACH), where the look-up table Δ_(col) may be constructed using theprocedure indicated in FIG. 20. In step S600, the processor 502 maycause k=1, and then the processor 502 of CO 500 may collect measurementsC_(v,[kT]) from the eNBs 105 a in geographic area 501 for furtherprocessing. Specifically, in each eNB 105 a, the processor 210 maycompute C_([kT])=(1/N_(sector))Σ_(v=1) ^(N) ^(sector) C_(v,[kT]) basedon Equations 16 and 17, where this information may then be stored inmemory 225 until the processor 502 requests this information. Then, instep S602, the processor 502 may compute an average contention ratio forthe network by calculating C_([kT])=(1/N_(sector))Σ_(v=1) ^(N) ^(sector)C_(v,[kT]).

In step S604, the processor 502 may then determine is the relationship|C_(max)−C_([kT])|>ϵ_(col) holds true, and if so the processor 502 thenmay adjust the RACH opportunity period based on the look-up table, whereτ_(RACH)(k)=τ_(RACH)(k−1)+Δ_(col)(τ_(RACH)(k−1), C_([kT])) in step S606.Otherwise, the processor 502 may determine thatτ_(RACH)(k)=τ_(RACH)(k−1), in step S608. Following steps S606 or S608,the processor may increment k (k=k+1), in step S610, and then return theprocess to step S600.

Comprehensive RACH Optimization Program (Algorithm 5):

The prior example embodiments (described above) considered anoptimization of individual RACH parameters as stand-alone processes.However, in reality, there are interactions among the RACH parameters.For example, changing a normalized threshold T_(RACH) may affect notonly a preamble false alarm ratio but also a preamble miss ratio, whichin turn may require a RACH target receive power P_(0,RACH) to beadjusted. Due to the dependencies between the RACH parameters, FIG. 22is a flowchart depicting a method of comprehensively optimizing multipleRACH parameters.

As depicted in FIG. 22, a method of comprehensive RACH optimization isprovided. Specifically, in step S650 the processor 502 may set k=1, andthen run an iteration of algorithm 2 (see FIGS. 11A-11C) to determine[P_(0,RACH)(k), P_(ramp,RACH)(k)]=Algorithm-2 (P_(0,RACH)(k−1),P_(ramp,RACH)(k−1), R_(max), M_(max,1), M_(max), γ_(P), γ_(R), γ_(N) ₁ ,ϵ_(M) ₁ , ϵ_(M) ₂ , ϵ_(R)). Then, in step S652, the processor 502 maydetermine if the relationship(P_(0,RACH)(k)=P_(0,RACH)(k−1))∧(P_(ramp,RACH)(k)=P_(ramp,RACH)(k−1))holds true, and if so then in step S654 the processor 502 may runalgorithm 3 (FIG. 18) to determine T_(RACH)(k), whereT_(RACH)(k)=Algorithm_3(T_(RACH)(k−1), F_(max), α_(FA), ϵ_(FA)).Otherwise, the method may progresses to step S660, where the processor502 increments (K=K+1), and return to step S650.

Following step S654, in step S656 the processor 502 may determine ifT_(RACH)(k)=T_(RACH)(k−1), and if so then in step S658 the processor 502may run algorithm 4 (FIG. 21) to determineτ_(RACH)(k)=Algorithm_4(τ_(RACH)(k−1), C_(max), Δ_(col), ϵ_(col)). Ifthe processor 502 determines that the inquiry of step S656 is negative,or if the processor 502 competes step S658, then the processor 502increments k (k=k+1) in step S660, and returns the process to step S650.

Decentralized RACH Optimization Procedures (Algorithm 6):

The example embodiments associated with FIG. 10 (a centralizedhierarchy) pertain to a hierarchy reliant on a central office 500 thatis able to collect measurements from eNBs 105 a within a particulargeographical area 501, where measurements are processed so that the eNBs105 a are updated with newly computed RACH parameters. However, such acentralized solution assumes that (1) communication interfaces areavailable for carrying information between the central office 500 andthe eNBs 105 a, (2) the deployment is homogeneous (i.e., all eNBs 105 aprovide a same coverage characteristics), and (3) the traffic ishomogeneous in the geographical area 501 being considered. In the casethat these conditions are not met, a decentralized solution may performbetter. FIG. 23 therefore depicts a decentralized hierarchy, where eacheNB 105 a has a same structure as shown in FIG. 4, and interferencemeasurements 901 are transmitted between neighboring eNBs 105 a viawireless interface 235.

Because changes in the RACH preamble detection threshold T_(RACH) andopportunity period τ_(RACH) have a low impact on a performance ofneighboring sectors, an optimization may be carried out independently ineach sector (i.e., in a decentralized fashion). In this scenario, theonly required changes to the methods of FIGS. 18 and 21 relate tosetting N_(sector)=1, where the optimizations are performed locally andindependently, on a sector-by-sector basis.

However, because variations in P_(0,RACH) and P_(ramp,RACH) affectinterference levels in neighboring sectors, a small amount ofcoordination between the sectors may improve performance. Specifically,neighboring sectors may be able to exchange information about theinterference levels that are experienced due to changes in P_(0,RACH)and P_(ramp,RACH). This exchange of interference information betweeneNBs 105 a may be done using the quantities R_(v,[kT])(p) as defined inEquation 9. Therefore, FIGS. 24A-24C provides a method implementing adecentralized solution for the optimization of P_(0,RACH) andP_(ramp,RACH) where any of the processors 210 of the eNB 105 a 1-105 a 5may perform the method steps (and, each of eNB 105 a 1-105 a 5 areidentical to the eNB 105 a of FIG. 4).

In step S700 of FIG. 24A, a processor 210 for any one of the sectors w(associated with any of the eNB 105 a 1-105 a 5) may set k=1, and x_(γ)₁ =x_(γ) ₂ =N_(γ), prior to collecting measurements M_(w,[kT])(l) andR_(v,[kT])(p) from the N_(neighbors) neighboring sectors of sector w. Instep S702, the processor 210 may then compute an average noise rise forthe N_(neighbors) neighboring sectors of sector w. A counter p maycorrespond to physical resource blocks where Messages 1 and/or 3 (FIG.26) may cause interference and N_(p) may be a total number of suchresource blocks for p=1 to N_(p), using the relationshipR_([kT])(p)=(1/N_(neighbor))Σ_(v=1) ^(N) ^(neighbor) R_(v,[kT])(p).

In step S704, the processor 210 may compute an overall average noiserise for the neighboring sectors using R_([kT])=(1/N_(p))Σ_(p=1) ^(N)^(p) R_([kT])(p). In step S706, the processor 210 may then calculate anaverage missed detection ratio for the second trial (and furthertrials), using the relationship M_(w,[kT])=(1/N_(l)−1))Σ_(l=2) ^(N) ^(l)M_(w,[kT])(l).

In step S708, the processor 210 may determine if|M_(max,1)−M_(w,[kT])(1)|>ϵ_(M) ₁ , and if so then the processor 210 mayadjust an adjust target receive power P_(0,RACH)(k), usingP_(0,RACH)(k)=P_(0,RACH)(k−1)−γ_(P)(M_(max,1)−M_(w,[kT])(1)), whereM_(1—)Flag(k)=1, in step S710. Otherwise, the processor 210 maycalculate P_(0,RACH)(k)=P_(0,RACH)(k−1), where M_(1—)Flag(k)=0 in stepS712.

Following step S710 or S712, the processor may determine adjust a powerramp P_(ramp,RACH)(k) by determining in step S714 whether|M_(max)−M_(w,[kT])|>ϵ_(M) ₂ , where if this relationship holds truethen in step S718 the processor 210 may adjust the power ramp bycalculatingP_(ramp,RACH)(k)=Q_(ramp){P_(ramp,RACH)(k−1)−γ_(R)(M_(max)−M_(w,[kT]))},where M_(2—)Flag(k)=1. Otherwise, the processor 210 may adjust the powerramp by determining P_(ramp,RACH)(k)=P_(ramp,RACH)(k−1), M_(2—)Flag(k)=0in step S716.

Following steps S716 or S718, the processor 210 may make adjustments inP_(0,RACH)(k) or P_(ramp,RACH)(k) to control the noise rise in theneighboring sectors by resetting flags R_(1—)Flag(k)=0 andR_(2—)Flag(k)=0 in step S720.

Following step S720, in step S724 the processor 210 may determine ifR_([kT])−R_(max)>ϵ_(R), and if so then in step S722 the processor 210may then determine if M_(w,[kT])(1)−M_(max,1)<ϵ_(M) ₁ in step S726. Ifstep S722 is answered in the affirmative, then in step S726 theprocessor 210 may determine P_(0,RACH)(k)=P_(0,RACH)(k−1)+γ_(N) ₁ (x_(γ)₁ )·(R_(max)−R_([kT])), where R_(1—)Flag(k)=1 (in step S726). Otherwise,in step S728 the processor 210 may determine M_(w,[kT])−M_(max)<ϵ_(M) ₂, in step S728.

In step S728, if the processor 210 makes the determination in theaffirmative, then in step S730 the processor 210 may make adjustments tocontrol a rise in noise by calculatingP_(ramp,RACH)(k)=Q_(ramp){P_(ramp,RACH)(k−1)+γ_(N) ₂ (x_(γ) ₂)·(R_(max)−R_([kT]))}, where R_(2—)Flag(k)=1. Otherwise, in step S732the method my continue where in step S732 the processor 210 may computean intensity of a “ping-pong” situation for a last N_(γ) samplingperiods, where x_(γ) ₁ =(Σ_(i=max(k-N) _(γ)_(,1))_(k)M_(1—)Flag(k))−(Σ_(i=max(k-N) _(γ) _(,1)) ^(k)R_(1—)Flag(k)),and x_(γ) ₂ =(Σ_(i=max(k-N) _(γ) _(,1))_(k)M_(2—)Flag(k))−(Σ_(i=max(k-N)_(γ) _(,1)) ^(k)R_(2—)Flag(k)). Then, in step S734, the counter mayincrement (K=K+1), in order to progress to a next sample period.

As in the centralized architecture shown in FIG. 10, a comprehensiveRACH parameter optimization procedure may be implanted for each sectorw. In the decentralized method for optimizing all RACH parameters, themethod of FIGS. 24A-24C may be used for controlling P_(0,RACH) andP_(ramp,RACH), and the decentralized methods of FIGS. 18 and 21 may beused for controlling T_(RACH) and τ_(RACH), respectively. FIG. 25 is aflowchart depicting a method of comprehensively optimizing RACHparameters in a decentralized hierarchy (as shown in FIG. 23).

In FIG. 25 (Algorithm 7), any of the processors 210 of the eNB 105 a1-105 a 5 may perform these method steps. In step S800, the processor210 may set k=1, and then perform an iteration of Algorithm 6 (FIGS.24A-24B) for a sector w, where [P_(0,RACH)(k),P_(ramp,RACH)(k)]=Algorithm_6(P_(0,RACH)(k−1), P_(ramp,RACH)(k−1),R_(max), M_(max,1), M_(max), γ_(P), γ_(R), γ_(N) ₁ , γ_(N) ₂ , ϵ_(M) ₁ ,ϵ_(M) ₂ , ϵ_(R)). It is noted that other algorithms may run only whenP_(0,RACH)(k) and P_(ramp,RACH)(k) are stable.

In step S802, the processor 210 may determine if(P_(0,RACH)(k)=P_(0,RACH)(k−1))∧(P_(ramp,RACH)(k)=P_(ramp,RACH)(k−1)),and if so then in step S804 the processor 210 may run an iteration ofthe decentralized version of Algorithm 3 (FIG. 18) for sector w, where[T_(RACH)(k)]=Algorithm_3(T_(RACH)(k−1), F_(max), α_(FA), ϵ_(FA)). Itshould be noted that other algorithms may run only when T_(RACH)(k) isstable. Otherwise, if the processor 210 does not determine step S802 tobe in the affirmative, then processor 210 may end the method in stepS810.

Following step S804, in step S806 the processor 210 may determine ifT_(RACH)(k)=T_(RACH)(k−1), and if so then in step S808 the processor mayperform an iteration of the decentralized version of Algorithm 4 (FIG.21) for sector w, where [τ_(RACH)(k)]=Algorithm_4(τ_(RACH)(k−1),C_(max), Δ_(col), ϵ_(col)). Otherwise, the processor 210 ends the method(in step S810).

Following step S808, the processor 210 ends the method (in step S810).

Example embodiments having thus been described, it will be obvious thatthe same may be varied in many ways. Such variations are not to beregarded as a departure from the intended spirit and scope of exampleembodiments, and all such modifications as would be obvious to oneskilled in the art are intended to be included within the scope of thefollowing claims.

1-10. (canceled)
 11. A user equipment, comprising: a memory withcomputer-readable instructions; and at least one first processorconfigured to read the computer-readable instructions, the at least onefirst processor being configured to, receive a broadcast message, thebroadcast message including random access channel, RACH, parameters,transmit a first message following receipt of the broadcast message, thefirst message including RACH preambles, receive a second message, thesecond message being an acknowledgement message that acknowledgesreceipt of the first message, transmit a third message, the thirdmessage including power settings based on a successful transmission ofthe first message, receive a fourth message, the fourth messageincluding contention resolution information that adjusts the RACHparameters, decode RACH parameters and the contention resolutioninformation to obtain decoded RACH parameters, and adjust an initialtransmit power of the user equipment to be a first transmit power basedon the decoded RACH parameters.
 12. The user equipment of claim 11,wherein the at least one first processor being is further configured to,perform an initial cell synchronization process with a first networknode prior to receiving the broadcast message.
 13. The user equipment ofclaim 11, wherein the at least one first processor being is furtherconfigured to, increase the initial transmit power of the user equipmentto be a second transmit power based on the decoded RACH parameters. 14.The user equipment of claim 11, wherein the RACH parameters include atleast one of an initial RACH target receive power and a RACH powerramp-up.
 15. A method performed by a user equipment, comprising:receiving, by at least one first processor of the user equipment, abroadcast message, the broadcast message including random accesschannel, RACH, parameters; transmitting, by the at least one firstprocessor, a first message following receipt of the broadcast message,the first message including RACH preambles; receiving, by the at leastone first processor, a second message, the second message being anacknowledgement message that acknowledges receipt of the first message;transmitting, by the at least one first processor, a third message, thethird message including power settings based on a successfultransmission of the first message; receiving, by the at least one firstprocessor, a fourth message, the fourth message including contentionresolution information that adjusts the RACH parameters; decoding, bythe at least one first processor, RACH parameters and the contentionresolution information to obtain decoded RACH parameters; and adjusting,by the at least one first processor, an initial transmit power of theuser equipment to be a first transmit power based on the decoded RACHparameters.
 16. The method of claim 15, further comprising: performingan initial cell synchronization process with a first network node priorto receiving the broadcast message.
 17. The method of claim 15, furthercomprising: increasing the initial transmit power of the user equipmentto be a second transmit power based on the decoded RACH parameters. 18.The method of claim 15, wherein the RACH parameters include at least oneof an initial RACH target receive power and a RACH power ramp-up.